Aims. To compare metabolic profiles and the long-term risk of metabolic dysfunction between women with previous gestational diabetes mellitus (pGDM) and women without pGDM (non-GDM) matched on age, prepregnancy body mass index (BMI), and parity. Methods. In total, 128 women with pGDM (median follow-up: 7.8 years) and 70 non-GDM controls (median follow-up: 10.0 years) completed a 2 h oral glucose tolerance test (OGTT) with assessment of glucose, C-peptide, insulin, and other metabolic measures. Additionally, anthropometrics, fat mass, and blood pressure were assessed and indices of insulin sensitivity and beta cell function were calculated. Results. The prevalence of type 2 diabetes mellitus (T2DM) was significantly higher in the pGDM group compared to the non-GDM group (26% vs. 0%). For women with pGDM, the prevalence of prediabetes (38%) and the metabolic syndrome (MetS) (59%) were approximately 3-fold higher than in non-GDM women (
p
’
s
<
0.001
). Both insulin sensitivity and beta cell function were significantly reduced in pGDM women compared to non-GDM women. Conclusion. Despite similar BMI, women with pGDM had a substantially higher risk of developing T2DM, prediabetes, and the MetS compared to controls. Both beta cell dysfunction and reduced insulin sensitivity seem to contribute to this increased risk.
Introduction:Uncertainty in interpreting laboratory results has been reported for 8% of tests, and may be even more common for infrequently ordered laboratory analyses, e.g. for rare diseases. We therefore aimed to evaluate whether a computerized clinical decision support (CCDS) tool can aid interpretation of specialized porphyria laboratory results, leading to faster diagnostic workup and fewer errors.
Method:Our CCDS tool consisted of an algorithm made in Statistical Analysis System® (SAS) that automatically generate an Excel® file with a suggested interpretation of the laboratory results.We evaluated the efficacy and accuracy of the CCDS tool, by comparing time and interpretation before and after introduction of the CCDS.
Results:Based on 965 workups investigated, we found that the required interpretation time by the medical doctor was reduced by 33% after introduction of the CCDS tool, Accuracy was 92% for diagnostic workups using the CCDS tool, compared to 88% for manual interpretation. None of the 8% (n=5) CCDS tool interpretation errors were critical. In conclusion, we proved that a CCDS tool for interpretation of laboratory results for a rare disease can be implemented safely and with a reduction in interpretation workload.
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